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UID:submissions.supercomputing.org_SC24_sess533_post227@linklings.com
SUMMARY:Scalable Motif Counting on Large-Scale Dynamic Graphs
DESCRIPTION:Ali Khan (University of North Texas)\n\nMotifs, small subgraph
 s of k vertices, such as triangles and cliques, are well studied for stati
 c networks. They are used to characterize different biological networks an
 d align different networks. Counting motifs reveal insights into the topol
 ogical structure of a network such as MPI event graphs. However, for large
  networks and motifs, computing these frequencies is computationally expen
 sive. Recent advances into sized-k or less motifs show promise but have di
 fficulty scaling. Moreover, counting the frequency of all sized-k or less 
 motifs on dynamic networks is still lacking. \nWe present a scalable metho
 d to compute the global edge-based frequencies of motifs of size 4 vertice
 s or less on a fully dynamic network. Instead of recomputing the counts fr
 om scratch, we update the frequencies based on only the changed edges. Our
  results show that our method is scalable and outperforms the benchmark re
 sults by 10 times.\n\nRegistration Category: Tech Program Reg Pass, Exhibi
 ts Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander Universit
 y, Erlangen-Nuremberg; Erlangen National High Performance Computing Center
 ); Sally Ellingson (University of Kentucky); and Alan Sussman (University 
 of Maryland)\n\n
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